Statistics and "Predicting" NBA champions

[ShoogarBear]

Warning: statistical talk follows, but I have tried to elminate any jargon and make it basketball-friendly. Also, I am not a formally trained statistician, but I have spent a lot of time over the last 20 years doing various kinds of statistical and nonlinear numerical analysis.

All this discussion on the use of statistics to rank teams made me go back and pull up an analysis I tried to do 2 years ago. (Not important, but the original reason I did this was I wanted to test a hypothesis that FT% was overrated for winning a championship, what was more important was to have an advantage in free throws attempted. Another thing I was interested in is whether you could show numbers that would show that a particular style of play won championships (i.e., using gimmick defenses to force a lot of turnovers was not superior to basic solid defense and limiting your own turnovers).)

The reason I never got around to publishing or posting it is that it turns out the true test you need to run on the numbers is not one that is readily available. However, the numbers I did come up with are somewhat interesting.

A few things you have to appreciate before doing any analysis like this:

1. These are correlations, not predictions. To demonstrate predictive power, you would have to show that this analysis can be used when you don’t already know who won. This is something Hollinger has never done, as far as I know. Supposedly Jeff Sagarin has with his ratings, but I’ve never seen those. This weakness applies to my analysis as well, it shows correlations, not predictions.

2. Absolute rankings (1st in the league, 2nd, etc.) are not meaningful. What is meaningful is the relative amount to which you are better than the teams below you. Take two hypothetical cases, in both the third-ranked team in the league in point differential wins the championship:

EXAMPLE ONE
1. Team A, +10.0 PPG
2. Team B, +9.0 PPG
3. Team C, +4.5 PPG (wins championship)

EXAMPLE TWO
1. Team A, +10.0 PPG
2. Team B, +9.9 PPG
3. Team C, +9.8 PPG (wins championship)

In the first example, 3rd ranked Team C winning the championship is a data point against point differential, because Teams A/B clearly were much better than the rest of the league in this stat. In the second example, Team C is essentially the same point differential as teams A/B, so it winning is actually data in favor of point differential.

I took the all data for the NBA from 1971-2004 (which is publicly available through the great work done by the people at databaseBasketball.com).

I tested all of the following variables for correlation to winning an NBA championship (I did not use 3-point stats because the database didn’t have complete listings for 3PA)
-win %
-Offensive stats: FGA, FGM, FG%, FTA, FTM, FT%, Oreb, Dreb, TotReb, Assists, PF, Stl, TO, Blk, Pts,
-Defensive stats: same as for offense
-Differentials: Pts, TotReb, OReb, DReb, Ast, Stl, TO, Blk, PF, FGA, FTA

The test I used is called logistic regression. As it turns out, it’s not really right test, for reasons I’ll explain later, but let’s look at the results anyway:

For winning a championship, the only numbers that showed significant correlation were, in order,
1) win %
2) total rebound differential.

If you excluded win% as a factor, then the significant factors were
1) Point differential
2) Total rebound differential
3) Blocks differential

Finally, to examine more cleanly the question of style of play, I took out both Win % and point differential and got the following factors
1) Defensive FG%
2) Defensive FT%

So Pop should be happy. I don’t have a good explanation for why Defensive FT% came out of there.

Now, this analysis is flawed because logistic regression isn’t really appropriate. The reason for it is the following:

Say I had 1000 guys and I wanted to analyzed what factors were important in determining who could dunk or not. I would have them all try to dunk and record if they were successful or not. I could then ook at height, weight, diet, race, left-handed vs. right-handed, shoe size, where they were born, diet, etc, plug them all into a logistic regression analysis, and come up with which factors were most important, and that would be a correct use of the test. Because whether Person #1 could dunk has no bearing on #345’s ability to dunk.

Now suppose instead I wanted to see which of the 1000 guys could jump the highest. Now they are competing against each other and the Highlander rule applies (“there can be only one”). Thus the outcome of one jumper, the winner, affects the outcome of all the others. In this case logistic regression isn’t the right test, and this is obviously analogous to the case of winning a championship.

I have ideas about what statistical assessment to perform, but it’s going to require some work and I’ve obviously never gotten around to it.

[Cry Havoc]

Well stated. It's nice to see people doing their homework.


It should also be noted as mentioned (not by me) in another thread that most of the teams who won the title with the best winning % were part of dynasties such as Jordan's Bulls or Magic's Lakers.

That said, I don't see anyone taking the title home this year other than the Mavs or the Spurs. Maybe an outside shot at the Suns if they learn to play defense, but probably not.

I said the same thing last year, of course, and I was dead wrong, mostly due to Wade. This year, he obviously is a big "?" right now, which leaves only 3 elite teams in the league, of which the Mavs have to the the favorites at the moment. I do feel that point differential is extremely important, but all three teams are close enough to negate that effect.

[timvp]

Very nicely done.

I've attempted something similar but on a smaller scale -- figuring out, with stats, what separates a championship Spurs team from a non-championship Spurs team in the Tim Duncan era. Meaning, could I find common statistical characteristics that linked the '99, '03 and '05 clubs.

It's a pretty interesting task, especially because the Spurs had some non-championship teams that put up impressive numbers across the board. I haven't put too much time into it, but I have yet to be successful.

One thing to consider as far as using stats to predict a championship, I would think that the most telling (and most accurate) stats would be if you could split the season in half and only use second half stats. There have been a lot of teams that peak late in the year that go on to win championships (Heat in '06, Spurs in '03, etc.) and by the time the playoffs roll around, the stats for the first half of the year I don't think would mean much. For example, the Heat were a much better defensive team in the second half of the year than the first half of the year, but if you just look at the end of season numbers, they look like one of the worst defensive teams to win a championship.

The main problem though is those numbers split like that don't exist :drunk

[FromWayDowntown]

The main problem though is those numbers split like that don't exist :drunk

There are ways to come up with those numbers (basketball reference now has every box score for every regular season game in the NBA since 1986-87) but it would be immensely time consuming to do so, unless someone can devise a program that can read a boxscore and quickly compile reams of information. I'll admit that when I do any sort of statistical analysis, I tend to do it by hand, which is both tedious and prone to error.

I'm interested in the notion of someone seeking to find some correlation between the weights that Hollinger assigns to his metrics and ultimate success. I generally discount power rankings of any stripe because they strike me as inherently flawed. But I have to say that my nerdy self is quite interested to know if Hollinger is talking out of his ass or if there might actually be some correlation in the numbers.

[Ockham]

Shoogarbear,

Thanks for the thoughtful analysis. I enjoyed the read. I'm surprised that defensive FT% was significant. (And that offensive FG% wasn't.)

Question: Where did you find information on blocks and offensive rebounds for 1971-1973? I didn't think they even started keeping track of those until 1974.

[ShoogarBear]

Very nicely done.

I've attempted something similar but on a smaller scale -- figuring out, with stats, what separates a championship Spurs team from a non-championship Spurs team in the Tim Duncan era. Meaning, could I find common statistical characteristics that linked the '99, '03 and '05 clubs.

It's a pretty interesting task, especially because the Spurs had some non-championship teams that put up impressive numbers across the board. I haven't put too much time into it, but I have yet to be successful.
Yep, this is exactly what you can do pretty easily with this kind of analysis AFTER you have a correct way of assessing the significance of all the variables.

For example, looking at the five major factors that came out of my (incorrect) analysis:



SA Spurs League Rankings over the years
Win% Pt.diff TRdiff Blkdiff DFG% DFT%
1977 9.5 12.0 21.0 5.0 22.0 2.0
1978 3.0 4.0 21.0 2.0 13.0 8.0
1979 4.5 1.0 14.0 4.0 4.0 10.0
1980 9.5 11.0 13.0 21.0 21.0 6.0
1981 6.0 6.0 1.0 4.0 8.0 4.0
1982 6.0 7.0 7.0 4.0 9.0 22.0
1983 4.5 6.0 2.0 11.0 11.0 22.0
1984 16.5 13.0 12.0 10.0 19.0 10.0
1985 11.5 10.0 6.0 14.0 17.0 21.0
1986 15.0 14.0 13.0 13.5 16.0 23.0
1987 20.0 21.0 12.0 21.0 22.0 5.0
1988 18.0 19.0 20.0 18.5 23.0 19.0
1989 22.5 22.0 21.0 22.0 18.0 18.0
1990 4.0 8.0 5.0 2.0 3.0 15.0
1991 5.5 6.0 1.0 2.0 1.0 10.0
1992 10.5 9.0 5.0 3.0 1.0 24.0
1993 8.0 8.0 17.0 3.0 4.0 26.0
1994 6.5 4.0 1.0 5.0 4.0 4.0
1995 1.0 4.0 3.0 10.0 7.0 2.0
1996 4.0 4.0 15.0 3.0 3.0 6.0
1997 27.0 28.0 21.0 16.0 25.0 16.0
1998 6.5 8.0 4.0 1.0 1.0 16.0
1999 1.5 1.0 8.0 2.0 1.0 2.0
2000 5.5 3.0 5.0 4.0 5.0 3.0
2001 1.0 1.0 6.0 2.0 2.0 9.0
2002 2.5 3.0 12.0 3.0 4.0 29.0
2003 1.5 3.0 12.0 5.0 3.0 24.0
2004 3.0 1.0 2.0 3.0 1.0 9.0



Highly ranked Spurs teams that didn't win it:
1979: #1 in point differential, lost to the Bullets in Game 7 of the ECF
1981: #1 in rebounding diff. Had a cakewalk to the NBA Finals when Houston upset LA in the first round, but instead lose game 7 to Moses & Co. in the WCSF.
1991: #1 in rebounding diff and #1 in DFG%. Strong team but Nellie goons it up and depants Larry Brown in the first round.
1992: #1 in DFG%. Lost to PHX in first round.
1994: #1 in rebounding diff. Lost to Utah in first round.
1995: #1 record, strong across the board EXCEPT for relatively weak DFG% ranking. Got Dreamshaked out of the WCF.
2001: #1 record and point diff, #2 DFG%. Destroyed by Lakers in WCF.
2004: top 3 across the board. Maybe stastically the Spurs' strongest team. Lost to PJax's coaching and 0.4*.


Spurs teams that won it (2005 stats not included)
1999: tied for #1 record, #1 in point diff., #1 in DFG%
2003: tied for #1 record, #3 in point diff, #3 in DFG%

Note that the Spurs have been strong in DFG% since DRob arrived, even despite Bob Hill's coaching.



One thing to consider as far as using stats to predict a championship, I would think that the most telling (and most accurate) stats would be if you could split the season in half and only use second half stats. There have been a lot of teams that peak late in the year that go on to win championships (Heat in '06, Spurs in '03, etc.) and by the time the playoffs roll around, the stats for the first half of the year I don't think would mean much. For example, the Heat were a much better defensive team in the second half of the year than the first half of the year, but if you just look at the end of season numbers, they look like one of the worst defensive teams to win a championship.

The main problem though is those numbers split like that don't exist :drunkI agree. In fact, if those number were avialble, it would be straightforward to test whether they were a better correlation than the first-half or overall record.

[ShoogarBear]

There are ways to come up with those numbers (basketball reference now has every box score for every regular season game in the NBA since 1986-87) but it would be immensely time consuming to do so, unless someone can devise a program that can read a boxscore and quickly compile reams of information. I'll admit that when I do any sort of statistical analysis, I tend to do it by hand, which is both tedious and prone to error. If those boxscores can be downloaded into a standardized text format, if would be very easy to write a macro to read them all in and generate the desired information.

Back around 1988, I was part of a group of ~15 basketball freaks on the USENET groups (pre-Web) that who daily compiled every NBA boxscore to plain text files so that they could be used to generate statistical analysis. We uploaded them to an archive site at Wash U. It was pretty time-consuming, however, and petered out after two years. One of the guys was Doug Steele, who kept with it and now has one of the best comprehensive NBA and MLB stats site at www.dougstats.com (http://www.dougstats.com). I laugh when I go there because he's still using the same basic format for his data files that we designed back then.

[Guru of Nothing]

How do statistics account for Dick Bavetta?

[ShoogarBear]

Shoogarbear,

Thanks for the thoughtful analysis. I enjoyed the read. I'm surprised that defensive FT% was significant. (And that offensive FG% wasn't.) First, I'll reiterate that the methods were not really appropriate, so Def FT% may be an artifact. Second, the correlation may be significant not because it has basis in reality, but because the numbers just randomly happened to work out that way. Usually the test are performed so that you can say you think there's a less than 5% chance of that, but it's still a possibility.

Finally, maybe it's real and we just need to think harder about why that may be true.



Question: Where did you find information on blocks and offensive rebounds for 1971-1973? I didn't think they even started keeping track of those until 1974.The date I gave is wrong. The analysis only applies from 1974 on.

[ShoogarBear]

Hey, timvp, I see that dougstats.com actually has a separate listing of stats for the second half of the 05-06 NBA season. I don't seem them for previous years, but I suspect he could generate them.

[L.I.T]

Now that's some impressive work and an interesting topic. Like Shoog said, you Def FT% comes into play as a variable, and happens to be true, I really wonder why? Could it be something as simples as an indication of 'clutchiness'? Since you indicated that Def FG% and Def FT% are the two primary indications of success when pt. diff and rebounding diff are thrown out, was there a correlation between those two factors and, let's say, the win % for games decided by 5 pts or less? Or was there a greater correlation between those two factors and teams who lead the league in point differential?

Have you looked into converting your analysis into a model? Like FWD said, it would be interesting to devise a model that would have some success in taking these variables and predicting post-season or championship success. Again, great job, it feeds my nerdy curiousity.

[Aggie Hoopsfan]

If those boxscores can be downloaded into a standardized text format, if would be very easy to write a macro to read them all in and generate the desired information.

Forget the macro. If you can get it in a text format I can parse it into a database and start crunching numbers :nerd

[FromWayDowntown]

Frankly, I have no freakin' clue how to get those boxscores into text files. They're there for anyone who wants to try it, though.

[pad300]

...

For winning a championship, the only numbers that showed significant correlation were, in order,
1) win %
2) total rebound differential.

If you excluded win% as a factor, then the significant factors were
1) Point differential
2) Total rebound differential
3) Blocks differential

...


Note, this implies, as Hollinger suggests that Win % and Point Differential are strongly correlated. Many people were not clear on this on the other thread.

If I understand your post correctly, you simply used a binary variable for the objective variable (Championship/No Championship).

It might be interesting to expand this analysis, to see if, for example, if you calculated the regular season Win % and Point Differential, and turned success in the playoffs into a linearly scaled quantized variable eg.

Value Meaning
1 First Round out (Conference quarterfinals)
2 Second Round out (Conference semifinals)
3 Third Round out (Conference finals)
4 Fourth Round Out (Lost in NBA finals)
5 NBA Champion

You could then treat Post-season success as a quantized independent variable, and see which factor (regular season Win % or regular season Point Differential) Could explain a larger fraction of the variation in post-season success.

[SpursFanJ]

How do statistics account for Dick Bavetta?


yeah, how do stats account for Bennett Salvatore (known Spurs-killer)?

[Fabbs]

For winning a championship, the only numbers that showed significant correlation were, in order,
1) win %
2) total rebound differential.

1. What are the final reg season records as to placement of the eventual Champ? I'm too tired to run. Wonder how many 3rd, 4th, and 5th best records Titled? Worst record to ever title? From a pure stat point if the Spurs end up seeded 4th will they have quite a mountain to climb?

2. in the reg season? Because i ran the Championship contestants rebound differential (Vs each other in the Finals only) and Rebounds=Rings was found to be flawed.
Was only like 57%-43%, with many of the 57% "winners" being only by a few total rebounds.

[SRJ]

1. What are the final reg season records as to placement of the eventual Champ? I'm too tired to run. Wonder how many 3rd, 4th, and 5th best records Titled? Worst record to ever title? From a pure stat point if the Spurs end up seeded 4th will they have quite a mountain to climb?

This is my kind of question.

In NBA history, the average Winning Percentage for a champion is .707, which translates into a 58-24 record. On the average, that record is good for the second best record in the league, which may either be the #1 or #2 seed in conference (the best record may reside in the same conference, hence the possible #2 seed).

Since 1968, the first season to feature the 82 game schedule, the average winning percentage is .720 which translates to a 59-23 mark. As before, the average placement for this record is second best in the league. (The 2005 Spurs met both criteria exactly)

Best record (clear first or tied) won 30 of the 60 championships.

Second best (clear or tied) won 17 of the 60 championships.

Third best won four championships.

Fourth best also won four championships.

Fifth best won two championships.

Just three champions have finished in a place lower than fifth: the 1978 Washington Bullets (tied 8th, 44-38), the 1995 Houston Rockets (tied 10th, 47-35), and the 2004 Detroit Pistons (6th, 54-28). It should be noted that the Rockets and Pistons both made midseason acquisitions of All-Star caliber players (Clyde Drexler for Houston and Rasheed Wallace for Detroit), so it's possible that these teams finished their regular season at a substantially higher level than they started it.

Worst record to ever win the title? The 1978 Washington Bullets. Their winning percentage was just 53.7% for a record of 44-38.

As of right now, the Spurs have the third best record in the NBA. If playoff seeding rules are the same as last year, that would place them in the fourth seed; if that holds, the Spurs would meet the Mavericks in the second round if both teams advanced. However, if the playoffs were starting now the Spurs would have to face the Houston Rockets, which looks to me like a long, tough matchup on its own. Getting through the Rockets and Mavericks to face the Jazz or Suns would certainly be a tough road to hoe. And I'm pretty sure the Western Conference champion would face the Detroit Pistons, another formidable team. To me it would rank as the toughest of the four championships if the Spurs could swing that.

[timvp]

As of right now, the Spurs have the third best record in the NBA. If playoff seeding rules are the same as last year, that would place them in the fourth seed; if that holds, the Spurs would meet the Mavericks in the second round if both teams advanced. However, if the playoffs were starting now the Spurs would have to face the Houston Rockets, which looks to me like a long, tough matchup on its own. Getting through the Rockets and Mavericks to face the Jazz or Suns would certainly be a tough road to hoe. And I'm pretty sure the Western Conference champion would face the Detroit Pistons, another formidable team. To me it would rank as the toughest of the four championships if the Spurs could swing that.

Luckily, the rules have changed. The Spurs are currently the three seed and wouldn't have to face the Mavs until the WCF.

[phyzik]

this is what seperates Spurs fans fron any other team... Especially Dallas fans..... Id be interested with the numbers THEY come up with besides this seasons wins vs Losses.... even half as detailed as this would suprise me, And its pretty simple math as well.

[SRJ]

Luckily, the rules have changed. The Spurs are currently the three seed and wouldn't have to face the Mavs until the WCF.

Thanks, and I'm glad to hear it. Hopefully, we won't have any tanking like the Clippers pulled last year.

[Fabbs]

This is my kind of question.

Best record (clear first or tied) won 30 of the 60 championships.

Second best (clear or tied) won 17 of the 60 championships.

Third best won four championships.

Fourth best also won four championships.

Fifth best won two championships.

Beautious! :madrun :blah
Any chance of giving us a year by year?
Or breaking it down to < 1980 > or so? :spin

[SRJ]

Beautious! :madrun :blah
Any chance of giving us a year by year?



Sure. First W-L is regular season record, second W-L is playoff record, third W-L is combined regular season and playoffs, fourth figure is point differential. [copies, pastes]

* - .750 total (season + playoffs) winning %
** - .800 total winning %
*** - .850 total winning %


THE 1940'S

1947 Philadelphia Warriors
35-25 (.583, 4th) 8-2 (.800) 43-27 (.614) +3.4 (3rd)
1948 Baltimore Bullets
28-20 (.583, T2) 9-1 (.900) 37-21 (.638) +3.9 (1st)
1949 Minneapolis Lakers
44-16 (.733, 2nd) 8-2 (.800) 52-18 (.743) +7.3 (1st)

THE 1950'S

1950 Minneapolis Lakers*
51-17 (.750, T2) 11-2 (.846) 62-19 (.765) +8.4 (1st)
1951 Rochester Royals
41-27 (.603, 3rd) 9-5 (.643) 50-32 (.610) +2.9 (3rd)
1952 Minneapolis Lakers
40-26 (.606, T2) 9-4 (.692) 49-30 (.620) +6.1 (1st)
1953 Minneapolis Lakers
48-22 (.686, 1st) 9-3 (.750) 57-25 (.695) +6.1 (1st)
1954 Minneapolis Lakers
46-26 (.639, 1st) 9-4 (.692) 55-30 (.647) +3.4 (2nd)
1955 Syracuse Nationals
43-29 (.597, T1) 7-4 (.636) 50-33 (.602) +1.4 (2nd)
1956 Philadelphia Warriors
45-27 (.625, 1st) 7-3 (.700) 52-30 (.634) +4.3 (1st)
1957 Boston Celtics
44-28 (.611, 1st) 7-3 (.700) 51-31 (.622) +5.3 (1st)
1958 St. Louis Hawks
41-31 (.569, T2) 8-3 (.727) 49-34 (.590) +1.3 (T3)
1959 Boston Celtics
52-20 (.722, 1st) 8-3 (.727) 60-23 (.723) +6.5 (1st)

THE 1960'S

1960 Boston Celtics*
59-16 (.787, 1st) 8-5 (.615) 67-21 (.761) +8.3 (1st)
1961 Boston Celtics
57-22 (.722, 1st) 8-2 (.800) 65-24 (.730) +5.6 (1st)
1962 Boston Celtics
60-20 (.750, 1st) 8-6 (.571) 68-26 (.723) +9.2 (1st)
1963 Boston Celtics
58-22 (.725, 1st) 8-5 (.615) 66-27 (.710) +7.2 (1st)
1964 Boston Celtics
59-21 (.738, 1st) 8-2 (.800) 67-23 (.744) +7.9 (1st)
1965 Boston Celtics*
62-18 (.775, 1st) 8-4 (.667) 70-22 (.761) +8.3 (1st)
1966 Boston Celtics
54-26 (.675, 2nd) 8-4 (.667) 62-30 (.674) +4.9 (1st)
1967 Philadelphia 76ers**
68-13 (.840, 1st) 11-4 (.733) 79-17 (.823) +9.4 (1st)
1968 Boston Celtics
54-28 (.659, 3rd) 12-7 (.632) 66-35 (.654) +4.1 (3rd)
1969 Boston Celtics
48-34 (.585, T5) 12-6 (.667) 60-40 (.600) +5.6 (T1)

THE 1970'S

1970 New York Knicks
60-22 (.732, 1st) 12-7 (.632) 72-29 (.713) +9.1 (1st)
1971 Milwaukee Bucks**
66-16 (.805, 1st) 12-2 (.857) 78-18 (.813) +12.2 (1st)
1972 Los Angeles Lakers**
69-13 (.841, 1st) 12-3 (.800) 81-16 (.835) +12.3 (1st)
1973 New York Knicks
57-25 (.695, 4th) 12-5 (.706) 69-30 (.697) +6.8 (4th)
1974 Boston Celtics
56-26 (.683, 2nd) 12-6 (.667) 68-32 (.680) +3.9 (3rd)
1975 Golden State Warriors
48-34 (.585, 4th) 12-5 (.706) 60-39 (.606) +3.3 (3rd)
1976 Boston Celtics
54-28 (.659, 2nd) 12-6 (.667) 66-34 (.660) +2.3 (4th)
1977 Portland Trail Blazers
49-33 (.598, T4) 14-5 (.737) 63-38 (.624) +5.5 (1st)
1978 Washington Bullets
44-38 (.537, T8) 14-7 (.667) 58-45 (.563) +0.9 (T7)
1979 Seattle Supersonics
52-30 (.634, 2nd) 12-5 (.706) 64-35 (.647) +2.3 (7th)

THE 1980'S

1980 Los Angeles Lakers
60-22 (.732, 2nd) 12-4 (.750) 72-26 (.735) +5.9 (2nd)
1981 Boston Celtics
62-20 (.756, T1) 12-5 (.706) 74-25 (.748) +5.9 (3rd)
1982 Los Angeles Lakers
57-25 (.695, 3rd) 12-2 (.857) 69-27 (.719) +4.8 (4th)
1983 Philadelphia 76ers**
65-17 (.793, 1st) 12-1 (.923) 77-18 (.811) +7.7 (1st)
1984 Boston Celtics
62-20 (.756, 1st) 15-8 (.652) 77-28 (.733) +6.5 (1st)
1985 Los Angeles Lakers*
62-20 (.756, 2nd) 15-4 (.790) 77-24 (.762) +7.3 (1st)
1986 Boston Celtics**
67-15 (.817, 1st) 15-3 (.833) 82-18 (.820) +9.4 (1st)
1987 Los Angeles Lakers**
65-17 (.793, 1st) 15-3 (.833) 80-20 (.800) +9.3 (1st)
1988 Los Angeles Lakers
62-20 (.756, 1st) 15-9 (.625) 77-29 (.726) +5.8 (2nd)
1989 Detroit Pistons*
63-19 (.768, 1st) 15-2 (.882) 78-21 (.788) +5.8 (4th)

THE 1990'S

1990 Detroit Pistons
59-23 (.720, T2) 15-5 (.750) 74-28 (.726) +6.0 (4th)
1991 Chicago Bulls*
61-21 (.744, 2nd) 15-2 (.882) 76-23 (.768) +9.0 (1st)
1992 Chicago Bulls*
67-15 (.817, 1st) 15-7 (.682) 82-22 (.789) +10.4 (1st)
1993 Chicago Bulls
57-25 (.695, 3rd) 15-4 (.790) 72-29 (.713) +6.3 (4th)
1994 Houston Rockets
58-24 (.707, 2nd) 15-8 (.652) 73-32 (.695) +4.3 (5th)
1995 Houston Rockets
47-35 (.573, T10) 15-7 (.682) 62-42 (.596) +2.1 (11th)
1996 Chicago Bulls***
72-10 (.878, 1st) 15-3 (.833) 87-13 (.870) +12.2 (1st)
1997 Chicago Bulls**
69-13 (.841, 1st) 15-4 (.790) 84-17 (.832) +10.8 (1st)
1998 Chicago Bulls
62-20 (.756, T1) 15-6 (.714) 77-26 (.748) +7.1 (3rd)
1999 San Antonio Spurs*
37-13 (.740, T1) 15-2 (.882) 52-15 (.776) +8.1 (1st)

THE 2000'S

2000 Los Angeles Lakers*
67-15 (.817, 1st) 15-8 (.652) 82-23 (.781) +8.5 (1st)
2001 Los Angeles Lakers
56-26 (.683, T2) 15-1 (.938) 71-27 (.725) +3.4 (7th)
2002 Los Angeles Lakers
58-24 (.707, T2) 15-4 (.790) 73-28 (.723) +7.1 (2nd)
2003 San Antonio Spurs
60-22 (.732, T1) 16-8 (.667) 76-30 (.717) +5.4 (3rd)
2004 Detroit Pistons
54-28 (.659, 6th) 16-7 (.696) 70-35 (.667) +5.8 (T2)
2005 San Antonio Spurs
59-23 (.720, T2) 16-7 (.696) 75-30 (.714) +7.8 (1st)
2006 Miami Heat
52-30 (.634, 5th) 16-7 (.696) 68-37 (.648) +3.9 (5th)

[Fabbs]

:worthy: